sendigR: an R package to leverage the value of CDISC SEND datasets for cross-study analysis.

The CDISC Standard for Exchange of Nonclinical Data (SEND) data standard has created new opportunities for collaborative development of open-source software solutions to facilitate cross-study analyses of toxicology study data. A public-private partnership between BioCelerate and the FDA/Center for Drug Evaluation and Research (CDER) was established in part to develop and publicize novel methods to facilitate cross-study analysis of SEND datasets. As part of this work in collaboration with the Pharmaceutical Users Software Exchange (PHUSE), an R package sendigR has been developed to enable users to construct a relational database from a collection of SEND datasets and then query that database to perform cross-study analyses.

Cross study analyses of SEND data: toxicity profile classification.

A SEND toxicology data transformation, harmonization, and analysis platform were created to improve the identification of unique findings related to the intended target, species, and duration of dosing using data from multiple studies. The lack of a standardized digital format for data analysis had impeded large-scale analysis of in vivo toxicology studies. The CDISC SEND standard enables the analysis of data from multiple studies performed by different laboratories.

Leveraging the Value of CDISC SEND Data Sets for Cross-Study Analysis: Incidence of Microscopic Findings in Control Animals.

Implementation of the Clinical Data Interchange Standards Consortium (CDISC)'s Standard for Exchange of Nonclinical Data (SEND) by the United States Food and Drug Administration Center for Drug Evaluation and Research (US FDA CDER) has created large quantities of SEND data sets and a tremendous opportunity to apply large-scale data analytic approaches. To fully realize this opportunity, differences in SEND implementation that impair the ability to conduct cross-study analysis must be addressed. In this manuscript, a prototypical question regarding historical control data (see Table of Contents graphic) was used to identify areas for SEND harmonization and to develop algorithmic strategies for nonclinical cross-study analysis within a variety of databases.

SEND harmonization & cross-study analysis: A proposal to better harvest the value from SEND data.

The Standard for Exchange of Nonclinical Data (SEND) identifies an approach for representing nonclinical data in a structured format which has been widely adopted by the pharmaceutical industry as it is required for data submission to the United States Food & Drug Administration (US FDA). The SEND Implementation Guide (SENDIG) allows for considerable flexibility in how data is represented; interpretation of these guidelines has led to significant variability in the approach to SEND dataset creation. The purposes of this manuscript are to identify common variability in certain SEND domains and to describe how variability can be managed to enable valuable cross-study analysis use cases.

Combining recombinant ribonuclease U2 and protein phosphatase for RNA modification mapping by liquid chromatography-mass spectrometry.

Ribonuclease (RNase) mapping of modified nucleosides onto RNA sequences is limited by RNase availability. A codon-optimized gene for RNase U2, a purine selective RNase with preference for adenosine, has been designed for overexpression using Escherichia coli as the host. Optimal expression conditions were identified enabling generation of milligram-scale quantities of active RNase U2.